Prediction of dynamic stiffness on rubber components considering preloads

Abstract

Nowadays rubber is used as various parts of vehicles to improve the noise, vibration and harshness (NVH) performance. Usually computer aided engineering (CAE) is commonly used to analyze various characteristics of vehicles. However, it is quite difficult to analyze the noise, vibration and harshness characteristics of an entire vehicle which includes lots of rubber components. There are two reasons. One is that the relation between load and deformation of rubber shows non-linear behavior. Another is that the preload is already acting on rubber components of complex shape when in assembly. Therefore repetitive tests are needed to clarify the dynamic characteristics of rubber components when preload works. It needs considerable time and effort if the shape of rubber components is changed to a large number of times in the development stages. In the present study, an efficient experimental method is suggested, which estimates the dynamic stiffness of a rubber component under various preload condition using the static and dynamic stiffness of the rubber material specimen and the static stiffness of the rubber component. It is assumed that the dynamic ratio -at any frequency, the ratio of the dynamic stiffness to the static stiffness of rubber -is constant at any frequency. The dynamic ratio is calculated due to the static and dynamic stiffness of the rubber material specimen that can be easily obtained from experimental tests. Then, the dynamic stiffness of a rubber component under any preload can be estimated by multiplying the dynamic ratio by the instantaneous static stiffness of the rubber component under the preload. In conservative approach, the dynamic stiffness of rubber is calculated by simply doing a multiplication of the static stiffness of rubber by 1.2~1.5 without consideration of the preload, due to difficulties of repetitive tests. Therefore, it is suggested that the dynamic stiffness of a rubber component can be precisely predicted in various preload conditions through only a few numbers of tests. Keywords